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GitHub GitHub-Copilot Exam Syllabus Topics:

Section	Weight	Objectives
Developer Use Cases for AI	14%–20%	- Integration with IDEs and workflows - Code generation and completion - Refactoring, documentation, and testing
Prompt Crafting and Prompt Engineering	9%–15%	- Iterative improvement and context management - Designing effective prompts
Privacy Fundamentals and Context Exclusions	15%–20%	- Configuring context exclusion rules - Protecting sensitive information
How GitHub Copilot Works and Handles Data	10%–15%	- Data processing, retention, and usage policies - Context collection and privacy controls - Architecture and underlying technology
Responsible AI	7%–20%	- Ethical and safe use of AI tools - Legal and compliance considerations
Testing with GitHub Copilot	9%–15%	- Generating tests and validation - Verifying output quality and correctness
GitHub Copilot Plans and Features	25%–31%	- Free, Pro, Business, Enterprise plans - Feature comparison and capabilities - Configuration and settings

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GitHub CopilotCertification Exam Sample Questions (Q99-Q104):

NEW QUESTION # 99
How does GitHub Copilot assist developers in reducing the amount of manual boilerplate code they write?

A. By refactoring the entire codebase to eliminate boilerplate code without developer input.
B. By predicting future coding requirements and pre-emptively generating boilerplate code
C. By suggesting code snippets that can be reused across different parts of the project.
D. By engaging in real-time collaboration with multiple developers to write boilerplate code.

Answer: C

Explanation:
GitHub Copilot identifies repetitive patterns and offers ready‑to‑use code snippets - such as class definitions, CRUD operations, or setup routines - so you don't have to write the same boilerplate from scratch.

NEW QUESTION # 100
What is a benefit of using custom models in GitHub Copilot?

A. Responses use the organization's LLM engine
B. Responses are guaranteed to be correct
C. Responses are faster to produce and appear sooner
D. Responses use practices and patterns in your repositories

Answer: D

Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and practices within your repositories. This results in suggestions that are more aligned with your organization's coding standards and conventions, improving the relevance and accuracy of the generated code.

NEW QUESTION # 101
What are two techniques that can be used to improve prompts to GitHub Copilot? (Select two.)

A. Provide links to supporting documentation
B. Provide specific success criteria
C. Provide insight on where to get the content from to get a response
D. Provide all information about the utilized files

Answer: B,C

Explanation:
To improve prompt quality for GitHub Copilot (especially Copilot Chat), good prompts should:
Clearly describe what "success" looks like
Specify where the context comes from (e.g., which file, function, or problem area) Be focused, not overloaded with unnecessary information

NEW QUESTION # 102
How can you improve the context used by GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

A. By adding the important files to your .gitconfig
B. By adding the full file paths to your prompt of important files
C. By adding variables (#selection) to your prompt
D. By opening the relevant tabs in your IDE

Answer: C,D

Explanation:
Improving the context for GitHub Copilot involves opening relevant files in your IDE to provide immediate context and adding relevant code snippets directly to your prompts to give Copilot specific examples and information.

NEW QUESTION # 103
How can GitHub Copilot assist in maintaining consistency across your tests?

A. By identifying a pattern in the way you write tests and suggesting similar patterns for future tests.
B. By providing documentation references based on industry best practices.
C. By writing the implementation code for the function based on context
D. By automatically fixing all tests in the code based on the context.

Answer: A

Explanation:
GitHub Copilot observes the style and structure of your existing tests and then suggests new tests following those same patterns, helping you keep your testing code consistent.

NEW QUESTION # 104
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